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I wish I could take credit for the title of today’s blog post, but it actually derives from a talk given by Mark Boguski of Harvard Medical School at a recent healthcare conference in Boston. Before you read any further, you should look up Mark’s CV on-line just for the fact that it is presented graphically which validates my thesis that pathologists are the super stars of big data within healthcare. More on that in a minute.

Dr. Mark Boguski of HMS and Genome Health Solutions

Most of my early career was spent designing, building and programming DNA sequencing devices. I was apprenticed under the Human Genome program and learned a great deal about engineering, programming, biological data and informatics. I’ve worked for both instrumentation and biotech companies who’s life blood was generating and interpreting massive amounts of life science data from expensive and complex machines. I spent 15 years working towards the $1000 genome. I’m a believer; which is why I was so intrigued by Mark’s talk.

Here’s Mark’s key message: The time of the $1000 genome meme is over. It served us well for years - driving advances in instrumentation, chemistry and biology. But now it is reducing clinical credibility. It’s time for it to leave the lexicon of healthcare; it has to go.

Here’s why he’s right: Every year there are 1.4 – 1.7 million new diagnoses of cancer in the US. Many of those cancer patients are familiar with the latest advances in healthcare technology reported in the media, including insights derived from medical data and personalized medicine. Here’s the challenge facing the treating physician: It costs a lot more than $1000 to sequence a genome. Sure, costs continue to fall but the price today to sequence [minimally] two genomes – from tumor and normal tissue, to analyze the data and have an oncologist interpret that data in terms of clinical recommendation - is between $25K and $100K. That cost is not covered by insurance. And most importantly, you have to know someone. If we set the total current sequencing capacity in the US against the number of new cancers each year we can only sequence a few percent of those patients. To get your cancer genome sequenced you have to have a friend at a genome center who can do the work, perform the analysis and work with a clinical oncologist to optimize therapy. This isn’t going to change any time soon.

The challenge for the clinical oncologist is the informed patient who says “I want to have my genome sequenced so we can pick the best drug. Here’s a $1000.” Here is a mismatch between science, healthcare and patient advocacy. So what’s the good news? In his presentation Mark talks about three waves of Medical Genomics. The first was “The human genome will yield many new drugs” – trust me, I lived that paradigm. The second is “Genome Wide Association Study (GWAS) data will help us manage common diseases” – getting closer but we have a ways to go. This by the way is a big data problem, associating GWAS data with patient clinical records – the very definition of translational medicine .

The third wave is “Precision diagnostics will lead to better outcomes”. Here is where we get real near-term progress in treating cancer (and a rich set of important and complex problems for healthcare big data scientists). In cancer, whole genome analysis will be done not once, but multiple times during the course of the disease for tumor subtyping , monitoring response to therapy and diagnosing the reasons for recurrences or therapeutic failures. It’s not about the $1000 genome. It’s about big data generation and analytics for insight creation over the clinical course of a patient’s journey through cancer. This is a message that Mark feels we need to get in front of patients and treating physicians and he’s doing that through his latest venture Genome Health Solutions .